Development and Validation of Measurement Traceability for In Situ Immunoassays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Immunoassays for protein analytes measured in situ support a $2 billion laboratory testing industry that suffers from significant interlaboratory disparities, affecting patient treatment. The root cause is that immunohistochemical testing lacks the generally accepted tools for analytic standardization, including reference standards and traceable units of measure. Until now, the creation of these tools has represented an insoluble technical hurdle. METHODS: We address the need with a new concept in metrology-that is, linked traceability. Rather than calculating analyte concentration directly, which has proven too variable, we calculate concentration by measuring an attached fluorescein, traceable to NIST Standard Reference Material 1934, a fluorescein standard. RESULTS: For validation, newly developed estrogen receptor (ER) calibrators were deployed in tandem with an array of 80 breast cancer tissue sections in a national external quality assessment program. Laboratory performance was assessed using both the ER standards and the tissue array. Similar to previous studies, the tissue array revealed substantial discrepancies in ER test results among the participating laboratories. The new ER calibrators revealed a broad range of analytic sensitivity, with the lower limits of detection ranging from 7310 to 74 790 molecules of ER. The data demonstrate, for the first time, that the variable test results correlate with analytic sensitivity, which can now be measured quantitatively. CONCLUSIONS: The reference standard enables precise interlaboratory alignment of immunohistochemistry test sensitivity for measuring cellular proteins in situ. The introduction of a reference standard and traceable units of measure for protein expression marks an important milestone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it